Decoding noises in HIV computational genotyping

Virology. 2017 Nov:511:249-255. doi: 10.1016/j.virol.2017.08.031. Epub 2017 Sep 14.

Abstract

Lack of a consistent and reliable genotyping system can critically impede HIV genomic research on pathogenesis, fitness, virulence, drug resistance, and genomic-based healthcare and treatment. At present, mis-genotyping, i.e., background noises in molecular genotyping, and its impact on epidemic surveillance is unknown. For the first time, we present a comprehensive assessment of HIV genotyping quality. HIV sequence data were retrieved from worldwide published records, and subjected to a systematic genotyping assessment pipeline. Results showed that mis-genotyped cases occurred at 4.6% globally, with some regional and high-risk population heterogeneities. Results also revealed a consistent mis-genotyping pattern in gp120 in all studied populations except the group of men who have sex with men. Our study also suggests novel virus diversities in the mis-genotyped cases. Finally, this study reemphasizes the importance of implementing a standardized genotyping pipeline to avoid genotyping disparity and to advance our understanding of virus evolution in various epidemiological settings.

Keywords: Epidemic surveillance; HIV; Molecular genotyping.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods*
  • Diagnostic Errors*
  • Genetic Variation
  • Genotype
  • Genotyping Techniques*
  • HIV / classification*
  • HIV / genetics*
  • Humans